Virtual spill-over effects: What social media has to do with relational values and global environmental stewardship

Langemeyer, Johannes; Calcagni, Fulvia

doi:10.1016/j.ecoser.2021.101400

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…For example, the text data associated with Flickr images can contain quite specific motivations for people's interactions (Havinga et al, 2021b). At the same time, social media also offers the opportunity to examine collective experiences through the presence of virtual communities (Langemeyer & Calcagni, 2022). It is therefore still important to use a variety of approaches to CES assessment with different epistemological underpinnings (Raymond et al, 2014; UK NEA, 2014).…”

Section: Discussionmentioning

confidence: 99%

Social media and deep learning reveal specific cultural preferences for biodiversity

et al. 2023

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Social media has created new opportunities to map cultural ecosystem services (CES) related to biodiversity at large scales. However, using these novel data to understand people's preferences in relation to these CES remains a challenge. To address this, we trained a deep learning model to capture people's interactions with selected flora and fauna on Flickr as a cultural service related to biodiversity and compared this with citizen science data on iNaturalist, with photos of individual species considered as human–species interactions. After mapping the distribution of people's interactions in Great Britain on Flickr and iNaturalist, we find significant spatial differences in people's preferences on the two platforms. Using a second, pretrained deep learning model, we were also able to identify different preferences for species groups such as birds on social media versus citizen science. To better understand people's preferences, we also compared people's interactions with species richness and abundance for a group of 36 bird species, sometimes finding large differences between people's interactions and these ecological measures. Our findings demonstrate that social media can be used to include a wider range of preferences in CES assessments along‐side citizen science data. However, these preferences reflect only a limited first‐hand experience of biodiversity. Read the free Plain Language Summary for this article on the Journal blog.

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Section: Discussionmentioning

confidence: 99%

Social media and deep learning reveal specific cultural preferences for biodiversity

et al. 2023

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“…Recent research has proposed that challenges to conceptualizing and measuring CES can be overcome by analysing information shared on social media (Calcagni et al, 2019; Gliozzo et al, 2016; Wood et al, 2013). This idea is premised on the observation that people use social media platforms such as Twitter and Instagram to share descriptions and depictions of their cultural experiences and interactions with the surrounding environment (Di Minin et al, 2015; Ghermandi & Sinclair, 2019; Langemeyer & Calcagni, 2022). Additionally, since social media are often georeferenced—meaning they are tied to the location where the content is created—they provide a spatial record of human–ecosystem interactions.…”

Section: Introductionmentioning

confidence: 99%

An open‐source image classifier for characterizing recreational activities across landscapes

et al. 2022

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“…For example, the text data associated with Flickr images can contain quite specific motivations for peoples' interactions (Havinga et al, 2021b). At the same time, social media also offers the opportunity to examine collective experiences through the presence of virtual communities (Langemeyer and Calcagni, 2022). It is therefore still important to use a variety of approaches to CES assessment with different epistemological underpinnings (Raymond et al, 2014;UK NEA, 2014).…”

Section: Large-scale Ces Assessmentmentioning

confidence: 99%

“…A third culture-specific category, "supporting identities" is also proposed which relates more to the relational aspect of nature's contributions to people. Although not explored in this thesis, the advantage of crowdsourced data is that it provides a lot of information on these more relational interactions with nature (Langemeyer and Calcagni, 2022). In other research, for example, Flickr images and the associated text have been used to understand cultural properties such as digital identities (Davies, 2007) and peoples' sense of place (Jenkins et al, 2016;Wartmann et al, 2018).…”

Section: Ipbesmentioning

confidence: 99%

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Crowdsourced data and machine learning to measure cultural ecosystem services

Havinga

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Cultural ecosystem services (CES) are some of the most valuable contributions of ecosystems to human well-being. Nevertheless, these services are often underrepresented in ecosystem service assessments. Defining CES for the purposes of spatial quantification has been challenging because it has been difficult to spatially model CES. However, rapid increases in mobile network connectivity and the use of social media have generated huge amounts of crowdsourced data. This offers an opportunity to define and spatially quantify CES. We inventoried established CES conceptualisations and sources of crowdsourced data to propose a CES definition and typology for spatial quantification. Furthermore, we present the results of three spatial models employing crowdsourced data to measure CES on Texel, a coastal island in the Netherlands. Defining CES as information-flows best enables service quantification. A general typology of eight services is proposed. The spatial models produced distributions consistent with known areas of cultural importance on Texel. However, user representativeness and measurement uncertainties affect our results.Ethical considerations must also be taken into account. Still, crowdsourced data is a valuable source of information to define and model CES due to the level of detail available. This can encourage the representation of CES in ecosystem service assessments.

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Virtual spill-over effects: What social media has to do with relational values and global environmental stewardship

Cited by 6 publications

References 22 publications

Social media and deep learning reveal specific cultural preferences for biodiversity

Social media and deep learning reveal specific cultural preferences for biodiversity

An open‐source image classifier for characterizing recreational activities across landscapes

Crowdsourced data and machine learning to measure cultural ecosystem services

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